Systems and methods for detecting network security threat event patterns

ABSTRACT

Techniques and mechanisms are disclosed for a data intake and query system to generate “meta-notable” events by applying a meta-notable event rule to a collection of notable event data. A meta-notable event rule specifies one or more patterns of notable event instances defined by a set of notable event states and a set of transition rules (also referred to as association rules) indicating conditions for transitioning from one notable event state to another. The set of notable event states includes at least one start state and at least one end state. A meta-notable event is generated when a set of analyzed notable events satisfies a set of transition rules linking a start state to an end state (including transitions through any intermediary states between the start state and the end state).

TECHNICAL FIELD

Embodiments relate generally to computer network security. Morespecifically, embodiments relate to techniques for detecting andinvestigating suspicious activity involving components of one or morecomputer networks.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Many types of organizations today rely on networked systems of computingdevices for an increasingly wide variety of business operations. Thesenetworked systems often include computing devices ranging from varioustypes of endpoint devices (for example, desktop computers, workstations,laptop computers, tablet devices, and mobile devices) to network devicesand other components (for example, routers, firewalls, web servers,email servers, and so forth). The reliance on these types of systems hasplaced great importance on the ability to secure systems againstinternal and external security threats such as malware, viruses, andnetwork-based attacks.

Organizations commonly use security information and event management(SIEM) software, endpoint threat detection and response (ETDR)applications, and other similar applications to monitor computernetworks for occurrences of potential security threats. However,security threats are often multi-layered (for example, involving manydifferent types of applications, types network activity, and so forth)and may implicate many separate components within a networked system.Efficiently detecting sophisticated network security threats andremediating threat occurrences in these environments with existingsecurity applications remains a challenge.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIGS. 7A-7D illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIG. 8 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 9A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 9B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments;

FIG. 9D illustrates a user interface screen displaying both log data andperformance data in accordance with the disclosed embodiments;

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 11 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIGS. 12-14 illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIGS. 15-17 illustrate example visualizations generated by a reportingapplication in accordance with the disclosed embodiments;

FIG. 18 illustrates a directed graph representing a generic meta-notableevent rule including a set of notable event states and a set oftransition rules in accordance with the disclosed embodiments;

FIG. 19 illustrates a directed graph representing a meta-notable eventrule defining a particular type of network security attack in accordancewith the disclosed embodiments;

FIG. 20 is a flow diagram illustrating an example process for receivinginput defining a meta-notable event rule and for analyzing a collectionof notable events to detect meta-notable event instances in accordancewith the disclosed embodiments;

FIG. 21 is a block diagram illustrating an example system flow fordetecting meta-notable events in accordance with the disclosedembodiments;

FIG. 22 illustrates an example meta-notable event dashboard interface inaccordance with the disclosed embodiments;

FIG. 23 illustrates an example map display showing information aboutmeta-notable events identified based on an applied meta-notable eventrule in accordance with the disclosed embodiments;

FIG. 24 is a diagram illustrating an example probability treevisualization generated based on a set of detected meta-notable eventsin accordance with the disclosed embodiments;

FIG. 25 depicts an example meta-notable event dashboard including a nodesequence graph in accordance with the disclosed embodiments;

FIG. 26 depicts an example meta-notable event dashboard including aselected path of a node sequence graph in accordance with the disclosedembodiments; and

FIG. 27 illustrates a computer system upon which an embodiment may beimplemented.

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

-   -   1.0. General Overview    -   2.0. Operating Environment        -   2.1. Host Devices        -   2.2. Client Devices        -   2.3. Client Device Applications        -   2.4. Data Server System        -   2.5. Data Ingestion            -   2.5.1. Input            -   2.5.2. Parsing            -   2.5.3. Indexing        -   2.6. Query Processing        -   2.7. Field Extraction        -   2.8. Example Search Screen        -   2.9. Data Modelling        -   2.10. Acceleration Techniques            -   2.10.1. Aggregation Technique            -   2.10.2. Keyword Index            -   2.10.3. High Performance Analytics Store            -   2.10.4. Accelerating Report Generation        -   2.11. Security Features        -   2.12. Data Center Monitoring        -   2.13. Cloud-Based System Overview        -   2.14. Searching Externally Archived Data            -   2.14.1. ERP Process Features    -   3.0. Functional Overview        -   3.1. Security Investigations Overview        -   3.2. Meta-Notable Framework Overview        -   3.3. Applying Meta-Notable Event Rules to Notable Event Data        -   3.4 Displaying Meta-Notable Event Information    -   4.0. Example Embodiments    -   5.0. Implementation Mechanism—Hardware Overview    -   6.0. Extensions and Alternatives

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as by IP address), even thoughthe raw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more local areanetworks (LANs), wide area networks (WANs), cellular networks (e.g.,Long-Term Evolution (LTE), High Speed Packet Access (HSPA), 3G, andother cellular technologies), and/or networks using any of wired,wireless, terrestrial microwave, or satellite links, and may include thepublic Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HyperText Markup Language (HTML) documents,media content, etc. The communication between a client device 102 andhost application 114 may include sending various requests and receivingdata packets. For example, in general, a client device 102 orapplication running on a client device may initiate communication with ahost application 114 by making a request for a specific resource (e.g.,based on an HTTP request), and the application server may respond withthe requested content stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a Uniform ResourceLocator (URL) requested, a connection type (e.g., HTTP, HTTP Secure(HTTPS), etc.), a connection start time, a connection end time, an HTTPstatus code, request length, response length, request headers, responseheaders, connection status (e.g., completion, response time(s), failure,etc.), and the like. Upon obtaining network performance data indicatingperformance of the network, the network performance data can betransmitted to a data intake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby data intake and query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or Internet Protocol (IP) address of adevice that generated the data. A source field may contain a valueidentifying a source of the data, such as a pathname of a file or aprotocol and port related to received network data. A source type fieldmay contain a value specifying a particular source type label for thedata. Additional metadata fields may also be included during the inputphase, such as a character encoding of the data, if known, and possiblyother values that provide information relevant to later processingsteps. In an embodiment, a forwarder forwards the annotated data blocksto another system component (typically an indexer) for furtherprocessing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING” also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in FIG. 4) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “|” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application 501 running on theuser's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support server 503 to complain about the orderfailing to complete. The three systems 501, 502, and 503 are disparatesystems that do not have a common logging format. The order application501 sends log data 504 to the SPLUNK® ENTERPRISE system in one format,the middleware code 502 sends error log data 505 in a second format, andthe support server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in Fig.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

FIGS. 12, 13, and 7A-7D illustrate a series of user interface screenswhere a user may select report generation options using data models. Thereport generation process may be driven by a predefined data modelobject, such as a data model object defined and/or saved via a reportingapplication or a data model object obtained from another source. A usercan load a saved data model object using a report editor. For example,the initial search query and fields used to drive the report editor maybe obtained from a data model object. The data model object that is usedto drive a report generation process may define a search and a set offields. Upon loading of the data model object, the report generationprocess may enable a user to use the fields (e.g., the fields defined bythe data model object) to define criteria for a report (e.g., filters,split rows/columns, aggregates, etc.) and the search may be used toidentify events (e.g., to identify events responsive to the search) usedto generate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 12 illustrates an example interactive data modelselection graphical user interface 1200 of a report editor that displaysa listing of available data models 1201. The user may select one of thedata models 1202.

FIG. 13 illustrates an example data model object selection graphicaluser interface 1300 that displays available data objects 1301 for theselected data object model 1202. The user may select one of thedisplayed data model objects 1302 for use in driving the reportgeneration process.

Once a data model object is selected by the user, a user interfacescreen 700 shown in FIG. 7A may display an interactive listing ofautomatic field identification options 701 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 702, the “SelectedFields” option 703, or the “Coverage” option (e.g., fields with at leasta specified % of coverage) 704). If the user selects the “All Fields”option 702, all of the fields identified from the events that werereturned in response to an initial search query may be selected. Thatis, for example, all of the fields of the identified data model objectfields may be selected. If the user selects the “Selected Fields” option703, only the fields from the fields of the identified data model objectfields that are selected by the user may be used. If the user selectsthe “Coverage” option 704, only the fields of the identified data modelobject fields meeting a specified coverage criteria may be selected. Apercent coverage may refer to the percentage of events returned by theinitial search query that a given field appears in. Thus, for example,if an object dataset includes 10,000 events returned in response to aninitial search query, and the “avg_age” field appears in 854 of those10,000 events, then the “avg_age” field would have a coverage of 8.54%for that object dataset. If, for example, the user selects the“Coverage” option and specifies a coverage value of 2%, only fieldshaving a coverage value equal to or greater than 2% may be selected. Thenumber of fields corresponding to each selectable option may bedisplayed in association with each option. For example, “97” displayednext to the “All Fields” option 702 indicates that 97 fields will beselected if the “All Fields” option is selected. The “3” displayed nextto the “Selected Fields” option 703 indicates that 3 of the 97 fieldswill be selected if the “Selected Fields” option is selected. The “49”displayed next to the “Coverage” option 704 indicates that 49 of the 97fields (e.g., the 49 fields having a coverage of 2% or greater) will beselected if the “Coverage” option is selected. The number of fieldscorresponding to the “Coverage” option may be dynamically updated basedon the specified percent of coverage.

FIG. 7B illustrates an example graphical user interface screen (alsocalled the pivot interface) 705 displaying the reporting application's“Report Editor” page. The screen may display interactive elements fordefining various elements of a report. For example, the page includes a“Filters” element 706, a “Split Rows” element 707, a “Split Columns”element 708, and a “Column Values” element 709. The page may include alist of search results 711. In this example, the Split Rows element 707is expanded, revealing a listing of fields 710 that can be used todefine additional criteria (e.g., reporting criteria). The listing offields 710 may correspond to the selected fields (attributes). That is,the listing of fields 710 may list only the fields previously selected,either automatically and/or manually by a user. FIG. 7C illustrates aformatting dialogue 712 that may be displayed upon selecting a fieldfrom the listing of fields 710. The dialogue can be used to format thedisplay of the results of the selection (e.g., to label the column to bedisplayed as “optional”).

FIG. 7D illustrates an example graphical user interface screen 705including a table of results 713 based on the selected criteriaincluding splitting the rows by the “component” field. A column 714having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row) occurs in theset of events responsive to the initial search query.

FIG. 14 illustrates an example graphical user interface screen 1400 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1401 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1402. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1406. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1403. A count of the number of successful purchases foreach product is displayed in column 1404. These statistics may beproduced by filtering the search results by the product name, findingall occurrences of a successful purchase in a field within the eventsand generating a total of the number of occurrences. A sum of the totalsales is displayed in column 1405, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 15 illustrates an example graphical user interface 1500 thatdisplays a set of components and associated statistical data 1501. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.). FIG.16 illustrates an example of a bar chart visualization 1600 of an aspectof the statistical data 1501. FIG. 17 illustrates a scatter plotvisualization 1700 of an aspect of the statistical data 1501.

2.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, the SPLUNK® ENTERPRISE system employs a numberof unique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 8 illustrates how a search query 802received from a client at a search head 210 can split into two phases,including: (1) subtasks 804 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 806 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 802, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 802 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 804, and then distributes searchquery 804 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4, or may alternativelydistribute a modified version (e.g., a more restricted version) of thesearch query to the search peers. In this example, the indexers areresponsible for producing the results and sending them to the searchhead. After the indexers return the results to the search head, thesearch head aggregates the received results 806 to form a single searchresult set. By executing the query in this manner, the systemeffectively distributes the computational operations across the indexerswhile minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards,and visualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The applicationenables the security practitioner to investigate and explore the data tofind new or unknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemslack the infrastructure to effectively store and analyze large volumesof security-related data. Traditional SIEM systems typically use fixedschemas to extract data from pre-defined security-related fields at dataingestion time and store the extracted data in a relational database.This traditional data extraction process (and associated reduction indata size) that occurs at data ingestion time inevitably hampers futureincident investigations that may need original data to determine theroot cause of a security issue, or to detect the onset of an impendingsecurity threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices, and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls, andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 9A illustrates anexample key indicators view 900 that comprises a dashboard, which candisplay a value 901, for various security-related metrics, such asmalware infections 902. It can also display a change in a metric value903, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 900 additionallydisplays a histogram panel 904 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 9B illustrates an example incident review dashboard 910 thatincludes a set of incident attribute fields 911 that, for example,enables a user to specify a time range field 912 for the displayedevents. It also includes a timeline 913 that graphically illustrates thenumber of incidents that occurred in time intervals over the selectedtime range. It additionally displays an events list 914 that enables auser to view a list of all of the notable events that match the criteriain the incident attributes fields 911. To facilitate identifyingpatterns among the notable events, each notable event can be associatedwith an urgency value (e.g., low, medium, high, critical), which isindicated in the incident review dashboard. The urgency value for adetected event can be determined based on the severity of the event andthe priority of the system component associated with the event.

2.12. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the task of creating various applications. Onesuch application is SPLUNK® APP FOR VMWARE® that provides operationalvisibility into granular performance metrics, logs, tasks and events,and topology from hosts, virtual machines and virtual centers. Itempowers administrators with an accurate real-time picture of the healthof the environment, proactively identifying performance and capacitybottlenecks.

Conventional data center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Example node-expansion operations are illustrated in FIG. 9C,wherein nodes 933 and 934 are selectively expanded. Note that nodes931-939 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 9Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 942 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCEMETRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOGDATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

2.13. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 2, the networkedcomputer system 1000 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system1000, one or more forwarders 204 and client devices 1002 are coupled toa cloud-based data intake and query system 1006 via one or more networks1004. Network 1004 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 1002 and forwarders204 to access the system 1006. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 1006 forfurther processing.

In an embodiment, a cloud-based data intake and query system 1006 maycomprise a plurality of system instances 1008. In general, each systeminstance 1008 may include one or more computing resources managed by aprovider of the cloud-based system 1006 made available to a particularsubscriber. The computing resources comprising a system instance 1008may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 1002 to access a web portal orother interface that enables the subscriber to configure an instance1008.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 1008), and may instead desireto make such configurations indirectly (e.g., using web-basedinterfaces). Thus, the techniques and systems described herein areapplicable to both on-premises and cloud-based service contexts, or somecombination thereof (e.g., a hybrid system where both an on-premisesenvironment such as SPLUNK® ENTERPRISE and a cloud-based environmentsuch as SPLUNK CLOUD™ are centrally visible).

2.14. Searching Externally Archived Data

FIG. 11 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1104 over network connections1120. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 11 illustratesthat multiple client devices 1104 a, 1104 b, . . . , 1104 n maycommunicate with the data intake and query system 108. The clientdevices 1104 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 11 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 1104 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1110. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1110, 1112. FIG. 11 shows two ERP processes 1110, 1112 thatconnect to respective remote (external) virtual indices, which areindicated as a Hadoop or another system 1114 (e.g., Amazon S3, AmazonElastic MapReduce (EMR), other Hadoop Compatible File Systems (HCFS),etc.) and a relational database management system (RDBMS) 1116. Othervirtual indices may include other file organizations and protocols, suchas Structured Query Language (SQL) and the like. The ellipses betweenthe ERP processes 1110, 1112 indicate optional additional ERP processesof the data intake and query system 108. An ERP process may be acomputer process that is initiated or spawned by the search head 210 andis executed by the search data intake and query system 108.Alternatively or additionally, an ERP process may be a process spawnedby the search head 210 on the same or different host system as thesearch head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1110, 1112 receive a search request from the searchhead 210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1110, 1112 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 1110, 1112 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1110, 1112 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices1114, 1116, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1104 may communicate with the data intake and querysystem 108 through a network interface 1120, e.g., one or more LANs,WANs, cellular networks, intranetworks, and/or internetworks using anyof wired, wireless, terrestrial microwave, satellite links, etc., andmay include the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.14.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the]streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.14. IT Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a search querythat derives a KPI value from the machine data of events associated withthe entities that provide the service. Information in the entitydefinitions may be used to identify the appropriate events at the time aKPI is defined or whenever a KPI value is being determined. The KPIvalues derived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to search query processing. AggregateKPIs may be defined to provide a measure of service performancecalculated from a set of service aspect KPI values; this aggregate mayeven be taken across defined timeframes and/or across multiple services.A particular service may have an aggregate KPI derived fromsubstantially all of the aspect KPI's of the service to indicate anoverall health score for the service.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be ‘critical,’‘warning,’ ‘normal,’ and ‘informational’. Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in agraphical user interface (GUI), for example. (The service dependencytopology is like a “map” showing how services are connected based ontheir dependencies.) The service topology may itself be depicted in aGUI and may be interactive to allow navigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a comma-separate values (CSV) file, anotherdelimited file, or a search query result set). The import may beGUI-mediated or processed using import parameters from a GUI-basedimport definition process. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be associated with a service by means of aservice definition rule. Processing the rule results in the matchingentity definitions being associated with the service definition. Therule can be processed at creation time, and thereafter on a scheduled oron-demand basis. This allows dynamic, rule-based updates to the servicedefinition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

3.0. Functional Overview

Approaches, techniques, and mechanisms are disclosed related to a new“meta-notable” event framework. According to embodiments describedherein, a meta-notable event framework allows users to define patternsof notable event data of interest and enables a security application todetect occurrences of defined notable event patterns. In an embodiment,a security application generates a new type of event, referred to hereinas a meta-notable event, in response to detecting a defined meta-notableevent pattern. At a high level, a meta-notable event identifies a set ofnotable events satisfying an associated notable event pattern and canprovide greater insight into the event data comprising the separatenotable events.

As used herein, a notable event refers to a type of event generated by adata intake and query system 108 to identify event data of particularinterest (for example, from event data stored by indexers 206 in one ormore data stores 208). Examples of generating and using notable eventsare described in at least Section 2.11. As indicated in Section 2.11 andelsewhere herein, a notable event can be generated in several waysincluding in response to a user manually selecting one or more eventsconsidered to be notable to the user (for example, the events can beselected from a search results list of events) or in response toidentification of an event or pattern of events matching the criteria ofa correlation search.

In an embodiment, a correlation search refers to a type of search thatevaluates events from one or more data sources to identify eventsmatching specified criteria or defined event relationships. For example,a correlation search can be configured to search for groups of eventsthat share or more common field values or other relationships, forindividual events that include one or more values exceeding a definedthreshold, or for events having any types of defined characteristics. Acorrelation search can be run continuously or at regular intervals (forexample, every hour) to search for notable events as new event dataarrives. The event data from which notable events are generated can bebased broadly on any type of machine-generated data related to theactivity, operation, or status of computing devices or other componentsof an information technology or security environment. For example, typesof computing devices and other components to which event data mightrelate include, but are not limited to, servers, networking devices,databases, desktop computers, workstations, laptop computers, tabletcomputers, mobile devices, applications, and so forth. Notable eventscan be stored in a “notable events index” which can be accessed togenerate various dashboards, visualizations, and alerts containinginformation related to the indexed notable events.

In the example context of a security application (for example, theSPLUNK® APP FOR ENTERPRISE SECURITY described above in Section 2.11), asecurity analyst might use a data intake and query system 108 to definevarious correlation searches to detect suspicious events or patterns ofevent data generated based on data sources related to a monitoredcomputer network. For example, the event data might include eventsindicating potential malware or virus infections, network-based attacks,user access attacks, or other security threats affecting one or morecomputing devices on the network. In this environment, an analyst mightcreate one correlation search to identify event data indicating that anyhost on the network experienced an unauthorized system access. Anothercorrelation search might be created to identify event data indicatingthat any host on the network communicated with an external server on aknown threat list. Yet another correlation search might identify eventdata indicating a potential occurrence of an unauthorized transfer ofsecure data outside of the network, and so forth.

Notable events generated by each of the separate correlation searchesdescribed above might be interesting to an analyst in insolation and canaid the analyst as part of a larger security investigation. However, ananalyst might often be even more interested in relationships amongdetected notable events and knowing which notable events are likely tobe most significant to an investigation. As one example, knowledge thata particular server experienced an unauthorized system access, that thesame server subsequently established connections with several externalservers on a known threat list, and further that the same server wasassociated with network traffic indicating an occurrence of secure dataexfiltration might provide a more comprehensive and useful view of alarge-scale network security attack. However, these types ofrelationships among notable events can be difficult to identify manuallyand are prone to being overlooked entirely if an analyst only has accessto lists of notable events generated by separate correlation searches,particularly as the number of notable events generated by separatecorrelation searches grows.

While users often are unable to use individual queries to expressintricate correlations and patterns of event data similar to the exampleabove, the meta-notable framework described herein enables users tointuitively specify such correlations and patterns by formallyexpressing event correlations and patterns in terms of underlyingnotable events. Referring again to the example correlation searchesabove, a user might define a meta-notable pattern to include detectionof a first notable event indicating an unauthorized system access at aserver X, followed by detection of a second notable event indicatingthat the same server X established a network connection with an externalserver Y on a threat list, followed by detection of a third notableevent indicating that the same server X sent data to the server Y withinfive minutes of the first notable event occurring. As described in moredetail hereinafter, a meta-notable event generally can be defined by anyone or more patterns of notable event states and relationships betweennotable event states (for example, based on field values shared amongnotable events, temporal constraints between notable events, and soforth).

According to one embodiment, a data intake and query system generatesmeta-notable events by applying a meta-notable event rule to acollection of notable events. A meta-notable event rule specifies one ormore patterns of notable event instances defined by a set of notableevent states and a set of transition rules (also referred to asassociation rules) indicating conditions for transitioning from onenotable event state to another. The set of notable event states includesat least one start state and at least one end state. A meta-notableevent is generated when a set of notable events satisfying a pluralityof transition rules linking a start state to an end state (includingtransitions through any intermediary states between the start state andthe end state) is identified in an analyzed collection of notableevents.

According to embodiments described herein, a network securityapplication generates and causes display of graphical user interfaces(GUIs) including various dashboards, visualizations, and otherinterfaces enabling users to specify meta-notable event rules and toview information related to meta-notable events identified based on oneor more specified meta-notable event rules. For example, a meta-notableevent rule creation GUI can include interface elements allowing users tocreate notable event states, to identify one or more of the notableevent states as end states, and to specify one or more transition rules.As another example, a meta-notable event display GUI can includeinterface elements graphically displaying meta-notable event rulepatterns, interface elements displaying various types of graphs andcharts providing information about identified meta-notable events, andinterface elements displaying event data associated with identifiedmeta-notable events.

Among other benefits, the described meta-notable framework increases thefidelity with which network security threats and attacks can bedetected, can provide better input to various types of risk scoringmodels, and can better inform analysts when conducting network securityinvestigations. The ability to define and detect meta-notable eventsimproves at least the technological processes of network security threatdetection and analysis by more efficiently detecting configurable eventdata patterns of interest to particular users and across different usecases. Although many of the examples provided herein are described inthe context of a network security application, the disclosedmeta-notable framework can be used to provide insight into any type ofevent-based data. In other aspects, embodiments of the inventionencompass a computer apparatus and a computer-readable medium configuredto carry out the foregoing.

3.1. Security Investigations Overview

As indicated above, one example use for a meta-notable event frameworkis in conjunction with a security application which can be used tomanage and analyze machine-generated data related to the status andoperation of various components within an information technology orsecurity environment. At a high level, a security application providesusers with visibility into security-relevant threat information bycapturing data generated by various networked components (for example,log data, network traffic data, application data, and so forth),generating time-stamped event data based on the captured data, andanalyzing the generated time-stamped event data for security-relatedissues. The analysis of the generated event data can provide a view ofan organization's security posture including information aboutindividual system components and broader system-wide securityinformation.

The use of a network security application often involves monitoring andaddressing security risks associated with a computing environment as therisks are detected. A network security investigation might begin, forexample, with a security analyst viewing an incident review dashboard910 depicted in FIGS. 9A and 9B and noticing one or more generatednotable events in an events list 914. These notable events might begenerated based on correlation searches configured to search forindications of unusual activity related to one or more computing devicesof a computer network. For example, various notable events might relateto event data indicating instances of a potential malware or virusinfection, brute-force login attacks, denial-of-service attacks, orother types of security threats. One or more of the identified notableevents may cause an analyst to desire more information about componentswithin the network potentially affected by the identified events.

The following is an example process that could be used to manage notableevents displayed on an incident review dashboard 910 or similarinterface. An administrative analyst might monitor the dashboard,sorting and performing high-level triage on newly-created notableevents. When the analyst determines that a notable event warrantsfurther investigation, the administrative analyst can assign the eventto a reviewing analyst to investigate the incident. The reviewinganalyst might then perform additional research and collect informationabout the assigned notable event using the fields and field actions inthe notable event. If the research indicates that the notable eventmerits further investigation, the analyst can assign the notable eventto a full investigation and perform the investigation. After thereviewing analyst addresses the cause of the notable event and anyremediation tasks have been escalated or solved, the analyst sets thenotable event status to resolved in the dashboard.

The notable event management process described above can help a securityteam manage notable events as the events are generated by a securityapplication. However, as the number of notable events generatedincreases, this process can become unwieldy and error-prone. Forexample, if many devices on a network become infected with malware basedon a network attack, event data collected from those devices may resultin the generation of many notable events indicating the presence of themalware across the network. This deluge of notable events can make itdifficult for an analyst to determine which notable events are goodcandidates for launching an investigation to determine an origin of theattack and to understand an extent of the attack.

3.2. Meta-Notable Framework Overview

According to embodiments described herein, a meta-notable eventframework can be used to help security analysts and other users betterunderstand and investigate notable events generated by a networksecurity application or other component of a data intake and querysystem. As indicated above, a meta-notable event can be defined by ameta-notable event rule including a set of notable event states and aset of transition rules indicating conditions for transitioning from onenotable event state to another. In an embodiment, the set of notableevent states and transition rules comprising a meta-notable event ruleconceptually form a finite-state machine that can be used to determinewhen a meta-notable event occurs based on an analyzed collection ofnotable events.

FIG. 18 illustrates a directed graph representing a generic meta-notableevent rule including a set of notable event states and a set oftransition rules. As shown, the example graph 1800 includes nodes1808-1818 and edges 1820-1830, where each of the nodes 1808-1818represents a separate notable event state and each of the edges1820-1830 represents a transition rule indicating conditions fortransitioning from one notable event state to another. The event statesrepresented by nodes 1808-1818 are grouped into three separate phases1802-1806, where each phase represents an organizational container ofone or more notable event states. The three phases 1802-1806 are merelyillustrative. In various embodiments, more or fewer phases can beincluded in a meta-notable event rule. The number of phases can bedetermined or associated with a particular meta-notable event rule basedon a particular condition, security threat, or event data occurrence tobe detected.

The nodes 1808-1818 and edges 1820-1830 of graph 1800 collectivelydefine several patterns of notable events linking a start node 1808 toone of end nodes 1816, 1818, where a meta-notable event occurs when aset of notable events satisfies any one of the patterns. For example,the graph 1800 includes a start state 1808. If an analyzed set ofnotable events includes a notable event satisfying a transition rulerepresented by edge 1820, the state of the graph 1800 transitions to a“state 1” represented by node 1810. In an embodiment, a notable eventsatisfies a transition rule if the notable event matches the criteriaspecified by the transition rule (for example, based on matching one ormore field values of the notable event) and the event is not excluded byany filters associated with the transition rule (for example, filtersimposing temporal or other constraints on matching notable events).Similarly, if the graph is in a “state 1” and the analyzed set ofnotable events also includes a second notable event satisfying atransition rule represented by edge 1824, the state of the graphtransitions to a “state 3” represented by node 1814, and so forth. Morespecific examples of notable event states and transition rules aredescribed below in reference to FIG. 19. As indicated above, ameta-notable event occurs if a set of notable events can be identifiedsatisfying a set of transition rules linking a start state (for example,the state represented by node 1808 in graph 1800) to an end state (forexample, the state represented by either node 1816 or 1818).

In an embodiment, an analyzed collection of notable events can includedistinct sets of notable events matching a same pattern of ameta-notable event rule. For example, an analyzed collection of notableevents might include a first set of notable events A, B and C and asecond set of notable events D, E, and F, each of which satisfies theset of transition rules linking the start state 1808 to the “end state1” 1816 via intermediate nodes 1810 and 1814 (that is, each of the setssatisfies the transition rules linking the notable event states“start”->“state 1”->“state 3”->“end state 1”). An analyzed collection ofnotable events can also include distinct sets of notable events matchingdifferent patterns of the same meta-notable event rule. For example, ananalyzed collection of notable events might include a first set ofnotable events A, B, and C satisfying the state pattern “start”->“state1”->“state 3”->“end state 1” and a second set of notable events D, E,and F satisfying the state pattern “start”->“state 2”->“state 3”->“endstate 2.” Further, a same notable event can be a part of multipledistinct sets of notable events satisfying a meta-notable event rule.For example, a first set of notable events A, B, and C, a second set ofnotable events A, B, and D, and a third set of notable events A, E, andF might each independently satisfy a set of transition rules linking thestart state 1808 to one of end states 1816, 1818, where each of the samenotable events A and B are included in multiple meta-notable events.

As indicated above, in one embodiment, the application of a meta-notableevent rule includes analyzing a collection of notable events todetermine whether any one or more sets of notable events from thecollection satisfies the meta-notable event rule. The analysis can beperformed against a static set of notable events or a continuouslyupdating set of notable events (that is, the analysis can be performedcontinuously or periodically over time as new notable events aregenerated and added to the collection). In an embodiment, the collectionof notable events can be generated by any number of separate correlationsearches or other notable event sources, where each correlation searchis configured to identify event data or patterns of event data and canbe configured to execute on a recurring basis. For example, one or moresearch heads 210 might be configured to execute the correlation searchesagainst event data collected from one or more data sources 202 andstored at one or more data stores 208.

In an embodiment, each of the phases 1802-1806 represents a logicalgrouping of notable event states. In one example, each of the phasesmight correspond to a phase of a threat modeling methodology thatprovides a structured approach to analyzing a wide variety of networksecurity threats. An example threat modeling methodology might includephases such as “Credential Access,” “Discovery,” “Lateral Movement,”“Execution,” “Exfiltration,” and “Command and Control,” where each ofthe phases is associated with various types of actions that typicallyoccur during that phase during a security attack (for example, actionssuch as a brute force login attempt, account creation, network sniffing,or other actions might typically occur during a “Credential Access”phase). In one embodiment, one or more of the notable event statescomprising a meta-notable event rule might be selected from a set ofnotable event phases associated with a pre-defined phase. For example, adefined “Credential Access” phase might be associated with a pre-definedset of correlation searches that search for event data indicatingparticular types of actions (brute force login attempts, networksniffing, and so forth) that typically occur during that phase of anattack. In other examples, users can define custom phases to logicallygroup one or more sets of notable event states added by a user to ameta-notable event rule.

FIG. 19 illustrates a directed graph representing a meta-notable eventrule defining a particular type of network security attack. For example,the meta-notable event rule represented by the graph 1900 includes a setof notable event states represented by nodes 1908-1918 and a set oftransition rules represented by edges 1920-1928 collectivelycorresponding to actions likely to occur during a data exfiltrationattack. The set of notable event states and transition rules of thegraph 1900 thus might have been selected by a user to enable detectionof potential data exfiltration attacks based on identified relationshipsamong a collection of notable events.

In FIG. 19, the nodes 1908-1918 are grouped into three separate phases1902-1906 labeled “Discovery,” “Execution,” and “Exfiltration,”respectively. As indicated above, these phases group sets of notableevent states corresponding to actions known to typically occur during adata exfiltration attack. In this example, each of the individualnotable event states might correspond to one or more separatecorrelation searches created or pre-configured to identify event datacorresponding to each of the separate actions and the meta-notable eventrule represented by the graph 1900 can be applied against notable eventsgenerated by these correlation searches or other sources.

The graph 1900 includes a first transition rule indicated by the edge1920 connecting the node 1908, representing an initial start state, tothe notable event state represented by node 1910. In this example, thetransition rule represented by edge 1920 might include criteriaspecifying that a matching notable event has a field value indicatingthat the event is associated with “File and directory discovery” (forexample, the rule might be stated as “rule_title=‘File and DirectoryDiscovery on $dest$’”). In this example rule, a value identifying anetwork address of an associated network component is stored using avariable “$dest$” for use in subsequent transition rules. If itdetermined that an analyzed collection of notable events includes anotable event satisfying this transition rule, the state of the graph1900 transitions to the state represented by node 1910.

The graph 1900 further includes a second transition rule indicated byedge 1922 connecting the node 1910 to node 1912. The transition rulerepresented by edge 1922 might, for example, specify criteria indicatingthat a matching notable event includes a field value indicating that theevent is associated with “Network configuration Discovery on $dest$” or“Network connections Discovery on $dest$.” The rule might also includecriteria specifying that a timestamp associated with the event indicatesthat the event occurred within one hour of the previous event satisfyingthe transition rule 1920, and further specifying that the “$dest$” valueof this event matches the “$dest$” value of the previous event. Thus,this example transition rule includes criteria matching a field value (aname of the event), matching a value associated the current eventagainst a value stored from a previous event (the “$dest” valueidentifying a network address), and a filter indicating a timeconstraint (that is, the current event is to occur within one hour ofthe previous event). In general, a transition rule can comprise anycombination of rules and filters to determine when a meta-notable eventrule transitions from one notable event state to another.

In this example, if an analyzed collection of notable events includes afirst notable event satisfying the transition rule represented by edge1920 and a second notable event satisfying the transition rulerepresented by edge 1922, the state of the graph 1900 transitions to thestate represented by node 1912. Analysis of a collection of notableevents can continue to determine whether any sets of three notableevents satisfy either transition rules 1920, 1922, and 1926 ortransition rules 1920, 1924, and 1928, linking the start state 1908 toone of the end states 1916 or 1918. As indicated above, each identifiedset of notable events satisfying the transition rules linking a startstate to an end state comprises a meta-notable event according toembodiments described herein.

3.3. Applying Meta-Notable Event Rules to Notable Event Data

FIG. 20 is a flow diagram 2000 illustrating an example process forreceiving input defining a meta-notable event rule and for analyzing acollection of notable events to detect meta-notable event instances. Thevarious elements of flow diagram 2000 may be performed in a variety ofsystems, including systems such as a data intake and query system 108described above. In an embodiment, each of the processes described inconnection with the functional blocks described below may be implementedusing one or more computer programs, other software elements, or digitallogic in any of a general-purpose computer or a special-purposecomputer, while performing data retrieval, transformation, and storageoperations that involve interacting with and transforming the physicalstate of memory of the computer.

At block 2002, input is received defining a meta-notable event rule. Inone embodiment, the input defining the meta-notable event rule includesinformation specifying a plurality of notable event states and aplurality of transition rules. As described above in reference to FIGS.18 and 19, for example, a collection of notable event states andtransition rules comprising a meta-notable event rule conceptually forma state diagram and can be used to determine when a collection ofnotable events includes one or more sets of notable events satisfyingthe meta-notable event rule. The input defining a meta-notable eventrule can be received by a component of a data intake and query system108 and stored for subsequent application against a collection ofnotable event data. As shown below in reference to FIG. 22, for example,a security application might generate one or more GUIs includinginterface elements that enable users specify the various components of ameta-notable event rule. In other embodiments, a data intake and querysystem can receive input defining a meta-notable event rule as aconfiguration file, a graphical depiction of the rule, or in any otherformat.

In one embodiment, a data intake and query system provides ameta-notable event rule language having a syntax that can be used todefine a meta-notable event rule. The syntax might include, for example,various constructs for defining the phases, notable event states, andtransition rules comprising a rule. As an example, a meta-notable eventrule language might allow users to specify a set of notable event statesusing the following syntax: “notable_event_states={phase #1: state #1,state #2; phase #2: state #3; phase #3: state #4, state #5}.” Here, theexample syntax indicates that one or more phases can be specified, eachseparated by a semicolon (for example, “phase #1: . . . ; phase #2: . .. ; phase #3: . . . ”), and that the notable event states associatedwith each of the phases can be specified in a comma-separated listfollowing the associated phase (for example, “phase #1: state #1, state#2” to indicate that a phase #1 includes states #1 and #2).

In an embodiment, a meta-notable event rule language can further includesyntax for specifying transition or association rules indicating toindicate when a meta-notable event rule transitions from one notableevent state to another. In one example, a syntax for specifyingtransition rules can include constructs to define matching rules, definefilters, and to logically combine various defined rules and filters. Ata high level, a matching rule specifies one or more conditions used toidentify notable events having certain characteristics (for example, toidentify notable events having a field value matching a specified value,having a field value exceeding a defined threshold, and so forth). Afilter comprises criteria that can be used to remove otherwise matchingnotable events from a results set (for example, to exclude otherwisematching events that occur outside of a defined time window). Logicaloperators (for example, “and” and “or”) can be also used to logicallycombine various matching rules and filters to define a transition rule.

As an example, one type of transition rule might be specified using thesyntax “smatch(example_string, example_field),” which returns notableevents having the value “example_string” as the value for the field“example_field” in the event. As another example, a transition rulemight be specified using the syntax “pmatch(previous_node,previous_field, match_field(optional)),” which returns notable eventshaving a same value in the field “match_field” as the previously matchednotable event “previous_node” has in the field “previous_field.” In oneembodiment, if no field is provided for the “match_field” parameter,then the rule defaults to using the same field as the “previous_field.”

As another example, a transition rule might be specified using thesyntax “tfilter(previous_node, seconds_before, seconds_after,example_expression),” which filters a set of notable events to excludethose events associated with a timestamp that is outside of a timewindow relative to a timestamp associated with a previously matchednotable event. For example, a tfilter rule can be used to filter fornotable events in adjacent notable event states that occur within fiveseconds of each other, within one hour of each other, at least tenminutes apart from one another, and so forth. The set of notable eventsfiltered by a tfilter rule is defined by the expression“example_expression.” In one example, a negative number can be specifiedfor “seconds_before” or “seconds_after” to not exclude any notableevents having a timestamp arbitrarily long before or arbitrarily longafter a current notable event, respectively. As yet another example, atransition rule might be specified using the syntax “not(expression),”which returns notable events not matched by the expression.

To illustrate an example, consider a user that desires to create a rulefor transitioning from a notable event state #1 to a state #2 based onidentifying the following condition: one or more notable events with a“rule_title” field having the value “Host With A Recurring MalwareInfection ($signature$ On $dest$),” where the value for the “$dest$”variable is the same as the value stored in a “dest” field of a notableevent causing the previous transition to state #1. In this example, thefollowing matching rule can be used: “pmatch(State #1, dest) andsmatch(Host With A Recurring Malware Infection ($signature$ On $dest$),rule_title).”

Consider again the example above, except where the user desires for thetransition rule also to be satisfied if the “rule_title” field has thevalue “Malicious PowerShell Process detected on $dest$.” For thisexample, the following matching rule can be used: “pmatch(State #1,dest) and (smatch(Host With A Recurring Malware Infection ($signature$On $dest$), rule_title) or smatch(Malicious PowerShell Process detectedon $dest$, rule_title)),” where the two different matching rules arejoined by an “or” operator.

As an additional example, a user might further desire to add atime-based filter to the transition rule specified above. For example,the user might desire for a transition from a state #1 to a state #2 tooccur, if the previous matching event was a “Malicious PowerShell . . .” event, then only if a matching event is associated with a timestampthat is within thirty minutes of the previously matched notable event,and if the previous matching event was a “Host With A Recurring MalwareInfection . . . ” event, then within any time period. In this example,the following matching rule can be used: pmatch(State #1, dest) and(tfilter(State #1,0,−1,smatch(Host With A Recurring Malware Infection($signature$ On $dest$), rule_title)) or tfilter(State #1,0,1800,smatch(Malicious PowerShell Process detected on $dest$, rule_title)).

As indicated above, in various embodiments, a collection of transitionrules can be specified by a user using any language similar to thatabove in a text file, via a graphical user interface, or using any otherinput mechanism. In other embodiments, a graphical meta-notable eventrule tool can be used (for example, to represent a meta-notable eventrule as a graph of nodes and edges) which translates a graphicalrepresentation of a meta-notable rule into the correspondingmeta-notable event rule language statements. In an embodiment, anapplication of a data intake and query system can execute a specifiedmeta-notable event rule, including specification of notable event statesand transition rules as shown above, by processing the syntax of therule and generating a corresponding set of queries or other mechanismsfor identifying notable events matching the specified rules.

At block 2004, a data intake and query system accesses a definedmeta-notable event rule to be applied to a collection of notable events.For example, a data intake and query system 104 might access themeta-notable event rule from storage, directly via input received in aGUI, or in any other manner. In an embodiment, accessing the definedmeta-notable event rule includes identifying at least the notable eventstates, event states identified as end states, and transition rulescomprising the meta-notable event rule.

At block 2006, a collection of notable events is analyzed using themeta-notable event rule accessed at block 2004. In one embodiment, theanalysis of a collection of notable events using the rule includesdetermining whether any one or more sets of notable events from thecollection satisfies a set of transition rules from the plurality oftransition rules linking a defined start state to a defined end state ofthe meta-notable event rule. For example, a search head 210 or othercomponent of a data intake and query system 108 can analyze the notableevents using search queries, string matching, logical comparisons, andany other operations and combinations thereof to determine whether anysets of notable events satisfy a meta-notable event rule. The set ofnotable events can be analyzed sequentially, out of order, in parallel,in groups, or in any fashion to perform the transition rule matching.

Referring again to FIG. 18, for example, a set of notable eventssatisfying a meta-notable event rule represented by the graph 1800includes notable events satisfying transition rules linking the node1808 representing a start state to one of nodes 1816, 1818, eachrepresenting an end state. For example, one set of notable events mightinclude a first notable event satisfying the transition rule representedby edge 1820 (advancing to the notable event state represented by node1810), a second notable event satisfying the transition rule representedby edge 1824 (advancing to the notable event state represented by node1814), and a third notable event satisfying the transition rulerepresented by edge 1828.

At block 2008, it is determined that a set of notable events satisfiesthe meta-notable event rule. As indicated above, it may be determinedthat a set of notable events satisfies the meta-notable event rule whenthe data intake and query system identifies a set of events satisfyingtransition rules linking a start state to a defined end state of therule.

At block 2010, a meta-notable event is generated and stored, where themeta-notable event identifies the set of notable events satisfying themeta-notable event rule. For example, the generated meta-notable eventmay include information identifying the event itself (for example, alabel associated with the meta-notable event, a timestamp indicatingwhen the event was generated, and so forth), identifying each of theassociated notable events satisfying the rule (for example, based onunique identifiers of each of the notable events, information copiedfrom the associated notable events, or any other data), summarized dataabout the associated notable events, and any other information about thenotable events or combinations thereof. In one embodiment, themeta-notable event identifying the set of notable events satisfying themeta-notable event rule is stored in a data store. For example, themeta-notable event can be stored in a data store 208 in an index sharedwith other events or in a separate “meta-notable event index.” In otherembodiments, a meta-notable event is stored only temporarily, forexample, for use in generating one or more GUIs displaying informationabout the generated meta-notable event.

FIG. 21 is a block diagram illustrating an example system flow fordetecting meta-notable events, further illustrating the processdescribed in relation to FIG. 20. The example meta-notable event system2100 illustrates various components and processes involved in generatingmeta-notable events, including notable event generation process 2012, ameta-notable event rule 2110, notable event mappings 2112 generated by ameta-notable event rule 2110, and an example meta-notable event 2114.

The notable event generation process 2102 illustrates that a set of oneor more correlation searches 2106 can be used to identify notable events2108 based on event data stored in one or more data stores 2104. In anembodiment, a meta-notable event rule 2110 can be applied against thecollection of notable events 2108 to identify any meta-notable eventinstances defined by the rule.

As indicated above, when analyzing a meta-notable event rule against acollection of notable events, multiple sets of notable events maysatisfy the rule and each individual notable event can be a part of oneor more meta-notable event sets. In FIG. 21, notable event mappings 2112illustrates example overlapping sets of notable events matching themeta-notable event rule 2110. The meta-notable event 2114 illustratesmore detailed information about one particular set of notable eventssatisfying the meta-notable event rule 2110. For example, each of theconstituent notable events of the meta-notable event 2114 is shownincluding information indicating a transition rule associated thenotable event, a user identifier associated with the notable event, andsource and destination network addresses. Although the notable events ofmeta-notable event 2114 are shown having a same set of fields, ingeneral, a collection of notable events analyzed by a meta-notable eventrule may or may not have a homogenous set of fields and event datastructure.

3.4. Displaying Meta-Notable Event Information

According to various embodiments, a data intake and query systemprovides graphical user interfaces (GUIs) including interface elementsenabling users to provide input to specify meta-notable event rules anddisplaying information related to detected meta-notable events based ondefined rules. The displayed information related to identifiedmeta-notable events can be used by security analysts and other users torefine existing meta-notable event rules, to conduct investigations intosecurity-related issues, and to analyze network security trends, amongother uses.

FIG. 22 depicts an example meta-notable event dashboard interface. Themeta-notable event dashboard 2200 can be used, for example, to receiveinput defining a meta-notable event rule and to display various types ofvisualizations of detected meta-notable events. For example, thedashboard 2200 includes an interface element 2202 which a user can useto provide input specifying a plurality of notable event states. In oneembodiment, input specifying notable event states can includeinformation grouping the notable event states into various phases. Forexample, the example syntax “Intrusion: n1, n2; Attack: n3” might beused to indicate that notable event states “n1” and “n2” are groupedtogether into a phase labeled “Intrusion” and a notable event state “n3”is included in a separate phase labeled “Attack.”

In an embodiment, an interface element 2204 can be used to identify oneor more notable event states as an end state. For example, in responseto a user specifying various notable event states using the interfaceelement 2202, one or more selectable elements corresponding to thestates might be displayed at element 2204. The identification of one ormore of the notable event states using interface element 2204 as endstates, for example, indicates that a meta-notable event is to begenerated each time the end state is reached during application of therule against a collection of notable events.

In an embodiment, an interface element 2206 can be used to specifytransition rules for a meta-notable event rule. In general, any syntaxor graphical elements can be used to specify various types of matchingcriteria, filters, and other transition rule elements as describedabove. Although the example dashboard 2200 illustrates creating ameta-notable event rule using the interface elements shown in FIG. 22,other types of graphical rule creation tools can be used. For example,another meta-notable event rule creation interface might includegenerating and displaying an editable graph, similar to those shown inFIGS. 18 and 19, where users can add, remove, and modify graphicalrepresentations of event states and transitions rules (for example,using nodes and edges) to create a rule.

FIG. 23 depicts an example map display showing information aboutmeta-notable events identified based on application of a meta-notableevent rule. The display 2300, for example, includes several nodesgrouped into phases 2302-2306. In this example, each of the nodesdisplayed in one of the phases 2302-2306 represents a notable event thatis part of a meta-notable event satisfying a meta-notable event rule.

As indicated above, each notable event of an analyzed collection can bea part of one or more meta-notable event sets satisfying a meta-notableevent rule. The example map diagram of FIG. 23 illustrates a displayshowing which notable events exist in multiple meta-notable event sets.For example, some of the notable event nodes of display 2300 include twoor more in-bound or out-bound edges indicating that the node is part ofmultiple meta-notable event sets. The display 2300 thus can provideinformation about which notable events have the most in-bound edges,out-bound edges, or both. This information can be used to indicate to ananalyst or other user which notable events might be of greatest interestduring a security investigation. The nodes in FIG. 23, for example, areshaded to indicate which nodes have the most in-bound links. Inparticular, the display 2300 indicates that the notable event labeled4873 is included in a relatively large number of meta-notable event sets(as indicated by the three separate in-bound edges). This might indicateto a security analyst, for example, that the notable event 4873 is agood notable event from which to start a security investigation. Inother examples, different types of graphical indications can be used toindicate frequently occurring notable events including size of thenodes, colors, shading, and so forth.

FIG. 24 is a diagram illustrating an example probability treevisualization generated based on a set of detected meta-notable events.For example, a set of meta-notable events can be used to determine ateach state of a meta-notable event rule what the likelihood is of atransition to a next notable event state of a plurality of possiblestates. These probabilities can be calculated by counting a number ofmeta-notable events that follow each of the different possible patterns.As shown in the visualization 2400, for example, these probabilities canbe mapped onto a display showing at each state (for example, at a“Discovery on 153.219.43.65” state), a probability that an attacktransitions to each of the possible next states (for example, a 90%chance of transitioning to a “Data Encrypted then Exfiltrated” state anda 10% chance of transitioning to a “Data Exfiltrated” state).

The information represented by a probability tree visualization can beused in a number of ways according to various embodiments. For example,the information might be used by a security analyst to triage in themiddle of a network security attack. That is, the security analyst canuse a probability tree visualization to assess the likelihood of anattacker's actions given the current state of an attack and canprioritize prevention efforts to minimize an expected amount of damage.The type of data illustrated by a probability tree visualization mightalso be useful to analyze data collected from a network securityhoneypot and to analyze new threats. In one embodiment, the probabilityinformation represented by a probability tree visualization could beused by network security tools (for example, firewalls, anti-virustools, and so forth) to preemptively take particular security actions inresponse to detecting an attack is in progress.

FIG. 25 depicts an example meta-notable event dashboard including a nodesequence graph. Similar to the dashboard of FIG. 22, in FIG. 25 anexample meta-notable event rule can be specified using interface element2502 to specify the meta-notable event states, interface element 2504 tospecify one or more end states, and interface element 2506 to specifythe transition rules. The dashboard 2500 further includes an interfaceelement 2508 used to apply the specified meta-notable event rule againsta collection of notable events.

The interface element 2510 illustrates a node sequence graphvisualization of meta-notable events identified based on an appliedmeta-notable event rule. For example, the node sequence graph depictsthree phases shown as vertical lines and labeled “Intrusion,” “Attack,”and “Demo,” and including nodes depicted as points on the vertical linesand edges connecting the nodes across the phases. In an embodiment,various graphical indications can be used to illustrate how manymeta-notable events satisfy each of the possible patterns across thephases, including a thickness of the edges between nodes, a color of theedges, and so forth.

The interface element 2512 shows a list of matching meta-notable events,including information about notable events comprising each of thematched meta-notable events. For example, the list of interface element2512 includes columns indicating, for each of the meta-notable eventsmatching the applied meta-notable event rule, hash values identifyingthe constituent notable events, node labels for the constituent notableevents, the phases associated with each of the notable events, arule_title associated with each of the notable events, a security domainassociated with each of the notable events, a timestamp for themeta-notable event, a duration of the meta-notable event, a userassociated with each of the notable events, network addresses associatedwith each of the notable events, and so forth. In an embodiment,selection of any one or more the displayed meta-notable events maygenerate an additional display with more information about themeta-notable event, the constituent notable events, or both.

FIG. 26 depicts an example meta-notable event dashboard similar to thatin FIG. 25 including a node sequence graph. In particular, the dashboard2600 includes an interface element 2602 displaying a node sequence graphin which one of the displayed paths 2604 is selected by a user (forexample, by clicking on the path or hovering over the path with an inputcursor). In one embodiment, the selection of a path in a node sequencegraph causes display of information indicating which meta-notable eventsmatch the selected path and a count of the events matching the path. Forexample, in response to selection of a path, the list of meta-notableevent matches in interface element 2606 can be updated to include onlythose that match the selected path.

4.0. Example Embodiments

Examples of some embodiments are represented, without limitation, in thefollowing clauses:

In an embodiment, a method or non-transitory computer readable mediumcomprises: accessing a meta-notable event rule, the meta-notable eventrule including: a plurality of notable event states, at least one of theplurality of notable event states corresponding to a correlation searchused to identify timestamped event data matching one or more searchcriteria, wherein at least one of the plurality of notable event statesis a start state and at least one of the plurality of notable eventstates is an end state, and a plurality of transition rules, eachtransition rule defining one or more criteria for transitioning betweentwo notable event states of the plurality of notable event states;analyzing a plurality of notable events using the meta-notable eventrule by determining whether any set of notable events from the pluralityof notable events satisfies a set of transition rules from the pluralityof transition rules linking a start state to an end state of themeta-notable event rule; and in response to determining that a set ofevents satisfies the meta-notable event rule, storing a recordidentifying the set of notable events satisfying the meta-notable eventrule.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one notable event of the plurality ofnotable events is generated based on execution of a correlation search.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the record identifying the set of notable eventssatisfying the meta-notable event rule is a timestamped event includingidentifiers of each notable event comprising the set of notable events.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the event data comprises a plurality of events, eachevent including a portion of raw machine data created by one or morecomponents of an information technology or security environment.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one of the analyzed plurality of notableevents indicates a potential network security threat involving at leastone computing device of an information technology or securityenvironment.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the set of notable events satisfying the meta-notableevent rule indicates a potential network security threat involving aplurality of computing devices of a computer network.

In an embodiment, a method or non-transitory computer readable mediumfurther comprises: causing display of a graphical user interface (GUI)including graphical elements used to receive input specifying theplurality of notable event states and the plurality of transition rules.

In an embodiment, a method or non-transitory computer readable mediumfurther comprises: causing display of a graphical user interface (GUI)including graphical elements used to display information related to thestored record identifying the set of notable events satisfying themeta-notable event rule.

In an embodiment, a method or non-transitory computer readable mediumfurther comprises: causing display of a graphical user interface (GUI)including graphical elements used to display information related to thestored record identifying the set of notable events satisfying themeta-notable event rule, the displayed information including informationstored as part of at least one of the notable events from the set ofnotable events satisfying the meta-notable event rule.

In an embodiment, a method or non-transitory computer readable mediumfurther comprises: causing display of a graphical user interface (GUI)including a graph showing relationships among the set of notable eventssatisfying the meta-notable event rule.

In an embodiment, a method or non-transitory computer readable mediumcomprises: causing display of a graphical user interface (GUI) includinggraphical elements used to display an indication, for at least oneparticular notable event of the set of notable events satisfying themeta-notable event rule, a total number of notable event sets satisfyingthe meta-notable event rule that include the particular notable event.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the set of transition rules links a start state tothe end state based on transitions to one or more intermediate notableevent states between the start state and the end state.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the correlation search used to identify the eventdata matching the one or more search criteria is executed periodically.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the correlation search used to identify the eventdata matching the one or more search criteria is executed on event datastored in a field-searchable data store.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the correlation search is executed to identify eventdata stored in a field-searchable data store matching the one or moresearch criteria, the stored event data comprising time stamped eventsthat include a portion of raw machine data created by a component of aninformation technology or security environment and which relates toactivity of the component in the information technology or securityenvironment.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the correlation search is executed to search forevent data stored in a field-searchable data store using a late-bindingschema.

In an embodiment, a method or non-transitory computer readable mediumfurther comprises: receiving raw machine data from components of aninformation technology or security environment; segmenting the receivedraw machine data into events, each event containing a portion of thecollected raw machine data; and for each event, determining a time stampfor the event, associating the time stamp with the event, and storingthe event in a field-searchable data store.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the event data includes a portion of raw machine datacreated by one or more components of an information technology orsecurity environment, and wherein at least one component of theinformation technology or security environment is one of a desktopcomputer, a workstation, a laptop computer, a tablet computer, a mobiledevice, a server, a database, a networking device, an application.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the event data includes a portion of raw machine datacreated by one or more components of an information technology orsecurity environment, and wherein the raw machine data includes one ormore of log data, wire data, server data, network data, file systeminformation, registry information, and information related to one ormore processes or services running on a device.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one transition rule of the plurality oftransition rules indicates a field value to be present in notable eventssatisfying the at least one transition rule.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one transition rule of the plurality oftransition rules indicates a field value to be matched between a firstnotable event matched by the at least one transition rule and a secondnotable event matched by a previous transition rule.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one transition rule of the plurality oftransition rules indicates filter excluding notable events matching oneor more filtering conditions.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one transition rule of the plurality oftransition rules includes a time-based filter excluding notable eventsmatching one or more time-based filtering conditions.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one transition rule of the plurality oftransition rules includes logical connectors connecting two or morematching or filtering conditions.

Other examples of these and other embodiments are found throughout thisdisclosure.

5.0. Implementation Mechanism—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be desktop computer systems,portable computer systems, handheld devices, networking devices or anyother device that incorporates hard-wired and/or program logic toimplement the techniques. The special-purpose computing devices may behard-wired to perform the techniques, or may include digital electronicdevices such as one or more application-specific integrated circuits(ASICs) or field programmable gate arrays (FPGAs) that are persistentlyprogrammed to perform the techniques, or may include one or more generalpurpose hardware processors programmed to perform the techniquespursuant to program instructions in firmware, memory, other storage, ora combination thereof. Such special-purpose computing devices may alsocombine custom hard-wired logic, ASICs, or FPGAs with custom programmingto accomplish the techniques.

FIG. 27 is a block diagram that illustrates a computer system 2700utilized in implementing the above-described techniques, according to anembodiment. Computer system 2700 may be, for example, a desktopcomputing device, laptop computing device, tablet, smartphone, serverappliance, computing mainframe, multimedia device, handheld device,networking apparatus, or any other suitable device.

Computer system 2700 includes one or more busses 2702 or othercommunication mechanism for communicating information, and one or morehardware processors 2704 coupled with busses 2702 for processinginformation. Hardware processors 2704 may be, for example, generalpurpose microprocessors. Busses 2702 may include various internal and/orexternal components, including, without limitation, internal processoror memory busses, a Serial ATA bus, a PCI Express bus, a UniversalSerial Bus, a HyperTransport bus, an Infiniband bus, and/or any othersuitable wired or wireless communication channel.

Computer system 2700 also includes a main memory 2706, such as a randomaccess memory (RAM) or other dynamic or volatile storage device, coupledto bus 2702 for storing information and instructions to be executed byprocessor 2704. Main memory 2706 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 2704. Such instructions, whenstored in non-transitory storage media accessible to processor 2704,render computer system 2700 a special-purpose machine that is customizedto perform the operations specified in the instructions.

Computer system 2700 further includes one or more read only memories(ROM) 2708 or other static storage devices coupled to bus 2702 forstoring static information and instructions for processor 2704. One ormore storage devices 2710, such as a solid-state drive (SSD), magneticdisk, optical disk, or other suitable non-volatile storage device, isprovided and coupled to bus 2702 for storing information andinstructions.

Computer system 2700 may be coupled via bus 2702 to one or more displays2712 for presenting information to a computer user. For instance,computer system 2700 may be connected via an High-Definition MultimediaInterface (HDMI) cable or other suitable cabling to a Liquid CrystalDisplay (LCD) monitor, and/or via a wireless connection such aspeer-to-peer Wi-Fi Direct connection to a Light-Emitting Diode (LED)television. Other examples of suitable types of displays 2712 mayinclude, without limitation, plasma display devices, projectors, cathoderay tube (CRT) monitors, electronic paper, virtual reality headsets,braille terminal, and/or any other suitable device for outputtinginformation to a computer user. In an embodiment, any suitable type ofoutput device, such as, for instance, an audio speaker or printer, maybe utilized instead of a display 2712.

One or more input devices 2714 are coupled to bus 2702 for communicatinginformation and command selections to processor 2704. One example of aninput device 2714 is a keyboard, including alphanumeric and other keys.Another type of user input device 2714 is cursor control 2716, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 2704 and for controllingcursor movement on display 2712. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Yetother examples of suitable input devices 2714 include a touch-screenpanel affixed to a display 2712, cameras, microphones, accelerometers,motion detectors, and/or other sensors. In an embodiment, anetwork-based input device 2714 may be utilized. In such an embodiment,user input and/or other information or commands may be relayed viarouters and/or switches on a Local Area Network (LAN) or other suitableshared network, or via a peer-to-peer network, from the input device2714 to a network link 2720 on the computer system 2700.

A computer system 2700 may implement techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 2700 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 2700 in response to processor 2704 executing one or moresequences of one or more instructions contained in main memory 2706.Such instructions may be read into main memory 2706 from another storagemedium, such as storage device 2710. Execution of the sequences ofinstructions contained in main memory 2706 causes processor 2704 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 2710.Volatile media includes dynamic memory, such as main memory 2706. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, an EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 2702. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 2704 for execution. Forexample, the instructions may initially be carried on a magnetic disk ora solid state drive of a remote computer. The remote computer can loadthe instructions into its dynamic memory and use a modem to send theinstructions over a network, such as a cable network or cellularnetwork, as modulate signals. A modem local to computer system 2700 canreceive the data on the network and demodulate the signal to decode thetransmitted instructions. Appropriate circuitry can then place the dataon bus 2702. Bus 2702 carries the data to main memory 2706, from whichprocessor 2704 retrieves and executes the instructions. The instructionsreceived by main memory 2706 may optionally be stored on storage device2710 either before or after execution by processor 2704.

A computer system 2700 may also include, in an embodiment, one or morecommunication interfaces 2718 coupled to bus 2702. A communicationinterface 2718 provides a data communication coupling, typicallytwo-way, to a network link 2720 that is connected to a local network2722. For example, a communication interface 2718 may be an integratedservices digital network (ISDN) card, cable modem, satellite modem, or amodem to provide a data communication connection to a corresponding typeof telephone line. As another example, the one or more communicationinterfaces 2718 may include a local area network (LAN) card to provide adata communication connection to a compatible LAN. As yet anotherexample, the one or more communication interfaces 2718 may include awireless network interface controller, such as a 802.11-basedcontroller, Bluetooth controller, Long Term Evolution (LTE) modem,and/or other types of wireless interfaces. In any such implementation,communication interface 2718 sends and receives electrical,electromagnetic, or optical signals that carry digital data streamsrepresenting various types of information.

Network link 2720 typically provides data communication through one ormore networks to other data devices. For example, network link 2720 mayprovide a connection through local network 2722 to a host computer 2724or to data equipment operated by a Service Provider 2726. ServiceProvider 2726, which may for example be an Internet Service Provider(ISP), in turn provides data communication services through a wide areanetwork, such as the world wide packet data communication network nowcommonly referred to as the “Internet” 2728. Local network 2722 andInternet 2728 both use electrical, electromagnetic or optical signalsthat carry digital data streams. The signals through the variousnetworks and the signals on network link 2720 and through communicationinterface 2718, which carry the digital data to and from computer system2700, are example forms of transmission media.

In an embodiment, computer system 2700 can send messages and receivedata, including program code and/or other types of instructions, throughthe network(s), network link 2720, and communication interface 2718. Inthe Internet example, a server 2730 might transmit a requested code foran application program through Internet 2728, ISP 2726, local network2722 and communication interface 2718. The received code may be executedby processor 2704 as it is received, and/or stored in storage device2710, or other non-volatile storage for later execution. As anotherexample, information received via a network link 2720 may be interpretedand/or processed by a software component of the computer system 2700,such as a web browser, application, or server, which in turn issuesinstructions based thereon to a processor 2704, possibly via anoperating system and/or other intermediate layers of softwarecomponents.

In an embodiment, some or all of the systems described herein may be orcomprise server computer systems, including one or more computer systems2700 that collectively implement various components of the system as aset of server-side processes. The server computer systems may includeweb server, application server, database server, and/or otherconventional server components that certain above-described componentsutilize to provide the described functionality. The server computersystems may receive network-based communications comprising input datafrom any of a variety of sources, including without limitationuser-operated client computing devices such as desktop computers,tablets, or smartphones, remote sensing devices, and/or other servercomputer systems.

In an embodiment, certain server components may be implemented in fullor in part using “cloud”-based components that are coupled to thesystems by one or more networks, such as the Internet. The cloud-basedcomponents may expose interfaces by which they provide processing,storage, software, and/or other resources to other components of thesystems. In an embodiment, the cloud-based components may be implementedby third-party entities, on behalf of another entity for whom thecomponents are deployed. In other embodiments, however, the describedsystems may be implemented entirely by computer systems owned andoperated by a single entity.

In an embodiment, an apparatus comprises a processor and is configuredto perform any of the foregoing methods. In an embodiment, anon-transitory computer readable storage medium, storing softwareinstructions, which when executed by one or more processors causeperformance of any of the foregoing methods.

6.0. Extensions and Alternatives

As used herein, the terms “first,” “second,” “certain,” and “particular”are used as naming conventions to distinguish queries, plans,representations, steps, objects, devices, or other items from eachother, so that these items may be referenced after they have beenintroduced. Unless otherwise specified herein, the use of these termsdoes not imply an ordering, timing, or any other characteristic of thereferenced items.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. Thus, the sole and exclusive indicatorof what is the invention, and is intended by the applicants to be theinvention, is the set of claims that issue from this application, in thespecific form in which such claims issue, including any subsequentcorrection. In this regard, although specific claim dependencies are setout in the claims of this application, it is to be noted that thefeatures of the dependent claims of this application may be combined asappropriate with the features of other dependent claims and with thefeatures of the independent claims of this application, and not merelyaccording to the specific dependencies recited in the set of claims.Moreover, although separate embodiments are discussed herein, anycombination of embodiments and/or partial embodiments discussed hereinmay be combined to form further embodiments.

Any definitions expressly set forth herein for terms contained in suchclaims shall govern the meaning of such terms as used in the claims.Hence, no limitation, element, property, feature, advantage or attributethat is not expressly recited in a claim should limit the scope of suchclaim in any way. The specification and drawings are, accordingly, to beregarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. A computer-implemented method, comprising:accessing a meta-notable event rule, the meta-notable event ruleincluding: a plurality of notable event states, at least one of theplurality of notable event states corresponding to a correlation searchused to identify timestamped event data matching one or more searchcriteria, wherein at least one of the plurality of notable event statesis a start state and at least one of the plurality of notable eventstates is an end state, and a plurality of transition rules, eachtransition rule defining one or more criteria for transitioning betweentwo notable event states of the plurality of notable event states;analyzing a plurality of notable events using the meta-notable eventrule by determining whether any set of notable events from the pluralityof notable events satisfies a set of transition rules from the pluralityof transition rules linking a start state to an end state of themeta-notable event rule; and in response to determining that a set ofevents satisfies the meta-notable event rule, storing a recordidentifying the set of notable events satisfying the meta-notable eventrule.
 2. The method of claim 1, wherein at least one notable event ofthe plurality of notable events is generated based on execution of acorrelation search.
 3. The method of claim 1, wherein the recordidentifying the set of notable events satisfying the meta-notable eventrule is a timestamped event including identifiers of each notable eventcomprising the set of notable events.
 4. The method of claim 1, whereinthe event data comprises a plurality of events, each event including aportion of raw machine data created by one or more components of aninformation technology or security environment.
 5. The method of claim1, wherein at least one of the analyzed plurality of notable eventsindicates a potential network security threat involving at least onecomputing device of an information technology or security environment.6. The method of claim 1, wherein the set of notable events satisfyingthe meta-notable event rule indicates a potential network securitythreat involving a plurality of computing devices of a computer network.7. The method of claim 1, further comprising causing display of agraphical user interface (GUI) including graphical elements used toreceive input specifying the plurality of notable event states and theplurality of transition rules.
 8. The method of claim 1, furthercomprising causing display of a graphical user interface (GUI) includinggraphical elements used to display information related to the storedrecord identifying the set of notable events satisfying the meta-notableevent rule.
 9. The method of claim 1, further comprising causing displayof a graphical user interface (GUI) including graphical elements used todisplay information related to the stored record identifying the set ofnotable events satisfying the meta-notable event rule, the displayedinformation including information stored as part of at least one of thenotable events from the set of notable events satisfying themeta-notable event rule.
 10. The method of claim 1, further comprisingcausing display of a graphical user interface (GUI) including a graphshowing relationships among the set of notable events satisfying themeta-notable event rule.
 11. The method of claim 1, further comprisingcausing display of a graphical user interface (GUI) including graphicalelements used to display an indication, for at least one particularnotable event of the set of notable events satisfying the meta-notableevent rule, a total number of notable event sets satisfying themeta-notable event rule that include the particular notable event. 12.The method of claim 1, wherein the set of transition rules links a startstate to the end state based on transitions to one or more intermediatenotable event states between the start state and the end state.
 13. Themethod of claim 1, wherein the correlation search used to identify theevent data matching the one or more search criteria is executedperiodically.
 14. The method of claim 1, wherein the correlation searchused to identify the event data matching the one or more search criteriais executed on event data stored in a field-searchable data store. 15.The method of claim 1, wherein the correlation search is executed toidentify event data stored in a field-searchable data store matching theone or more search criteria, the stored event data comprising timestamped events that include a portion of raw machine data created by acomponent of an information technology or security environment and whichrelates to activity of the component in the information technology orsecurity environment.
 16. The method of claim 1, wherein the correlationsearch is executed to search for event data stored in a field-searchabledata store using a late-binding schema.
 17. The method of claim 1,further comprising: receiving raw machine data from components of aninformation technology or security environment; segmenting the receivedraw machine data into events, each event containing a portion of thecollected raw machine data; and for each event, determining a time stampfor the event, associating the time stamp with the event, and storingthe event in a field-searchable data store.
 18. The method of claim 1,wherein the event data includes a portion of raw machine data created byone or more components of an information technology or securityenvironment, and wherein at least one component of the informationtechnology or security environment is one of a desktop computer, aworkstation, a laptop computer, a tablet computer, a mobile device, aserver, a database, a networking device, an application.
 19. The methodof claim 1, wherein the event data includes a portion of raw machinedata created by one or more components of an information technology orsecurity environment, and wherein the raw machine data includes one ormore of log data, wire data, server data, network data, file systeminformation, registry information, and information related to one ormore processes or services running on a device.
 20. The method of claim1, wherein at least one transition rule of the plurality of transitionrules indicates a field value to be present in notable events satisfyingthe at least one transition rule.
 21. The method of claim 1, wherein atleast one transition rule of the plurality of transition rules indicatesa field value to be matched between a first notable event matched by theat least one transition rule and a second notable event matched by aprevious transition rule.
 22. The method of claim 1, wherein at leastone transition rule of the plurality of transition rules indicatesfilter excluding notable events matching one or more filteringconditions.
 23. The method of claim 1, wherein at least one transitionrule of the plurality of transition rules includes a time-based filterexcluding notable events matching one or more time-based filteringconditions.
 24. The method of claim 1, wherein at least one transitionrule of the plurality of transition rules includes logical connectorsconnecting two or more matching or filtering conditions.
 25. Anon-transitory computer-readable storage medium storing instructionswhich, when executed by one or more processors, cause performance ofoperations comprising: accessing a meta-notable event rule, themeta-notable event rule including: a plurality of notable event states,at least one of the plurality of notable event states corresponding to acorrelation search used to identify timestamped event data matching oneor more search criteria, wherein at least one of the plurality ofnotable event states is a start state and at least one of the pluralityof notable event states is an end state, and a plurality of transitionrules, each transition rule defining one or more criteria fortransitioning between two notable event states of the plurality ofnotable event states; analyzing a plurality of notable events using themeta-notable event rule by determining whether any set of notable eventsfrom the plurality of notable events satisfies a set of transition rulesfrom the plurality of transition rules linking a start state to an endstate of the meta-notable event rule; and in response to determiningthat a set of notable events satisfies the meta-notable event rule,storing an record identifying the set of notable events satisfying themeta-notable event rule.
 26. The non-transitory computer-readablestorage medium of claim 25, wherein at least one notable event of theplurality of notable events is generated based on execution of acorrelation search.
 27. The non-transitory computer-readable storagemedium of claim 25, wherein the record identifying the set of notableevents satisfying the meta-notable event rule is a timestamped eventincluding identifiers of each notable event comprising the set ofnotable events.
 28. An apparatus, comprising: one or more processors; anon-transitory computer-readable storage medium coupled to the one ormore processors, the computer-readable storage medium storinginstructions which, when executed by the one or more processors, causesthe apparatus to: access a meta-notable event rule, the meta-notableevent rule including: a plurality of notable event states, at least oneof the plurality of notable event states corresponding to a correlationsearch used to identify timestamped event data matching one or moresearch criteria, wherein at least one of the plurality of notable eventstates is a start state and at least one of the plurality of notableevent states is an end state, and a plurality of transition rules, eachtransition rule defining one or more criteria for transitioning betweentwo notable event states of the plurality of notable event states;analyze a plurality of notable events using the meta-notable event ruleby determining whether any set of notable events from the plurality ofnotable events satisfies a set of transition rules from the plurality oftransition rules linking a start state to an end state of themeta-notable event rule; and in response to determining that a set ofevents satisfies the meta-notable event rule, store an recordidentifying the set of notable events satisfying the meta-notable eventrule.
 29. The apparatus of claim 28, wherein at least one notable eventof the plurality of notable events is generated based on execution of acorrelation search.
 30. The apparatus of claim 28, wherein the recordidentifying the set of notable events satisfying the meta-notable eventrule is a timestamped event including identifiers of each notable eventcomprising the set of notable events.